Sparse Portfolio Selection via Topological Data Analysis based Clustering
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929628457730048 |
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| author | Goel, Anubha Filipović, Damir Pasricha, Puneet |
| author_facet | Goel, Anubha Filipović, Damir Pasricha, Puneet |
| contents | This paper uses topological data analysis (TDA) tools and introduces a data-driven clustering-based stock selection strategy tailored for sparse portfolio construction. Our asset selection strategy exploits the topological features of stock price movements to select a subset of topologically similar (different) assets for a sparse index tracking (Markowitz) portfolio. We introduce new distance measures, which serve as an input to the clustering algorithm, on the space of persistence diagrams and landscapes that consider the time component of a time series. We conduct an empirical analysis on the S\&P index from 2009 to 2022, including a study on the COVID-19 data to validate the robustness of our methodology. Our strategy to integrate TDA with the clustering algorithm significantly enhanced the performance of sparse portfolios across various performance measures in diverse market scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16920 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse Portfolio Selection via Topological Data Analysis based Clustering Goel, Anubha Filipović, Damir Pasricha, Puneet Portfolio Management Machine Learning Statistical Finance This paper uses topological data analysis (TDA) tools and introduces a data-driven clustering-based stock selection strategy tailored for sparse portfolio construction. Our asset selection strategy exploits the topological features of stock price movements to select a subset of topologically similar (different) assets for a sparse index tracking (Markowitz) portfolio. We introduce new distance measures, which serve as an input to the clustering algorithm, on the space of persistence diagrams and landscapes that consider the time component of a time series. We conduct an empirical analysis on the S\&P index from 2009 to 2022, including a study on the COVID-19 data to validate the robustness of our methodology. Our strategy to integrate TDA with the clustering algorithm significantly enhanced the performance of sparse portfolios across various performance measures in diverse market scenarios. |
| title | Sparse Portfolio Selection via Topological Data Analysis based Clustering |
| topic | Portfolio Management Machine Learning Statistical Finance |
| url | https://arxiv.org/abs/2401.16920 |